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COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 19 No. 1 (2025) · hlm. 35–50

DOI
10.30598/barekengvol19iss1pp35-50
ISSN
2615-3017, 1978-7227
Terbit
2025-01-13
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

In line with the Sustainable Development Goals (SDGs), the Infant Mortality Rate (AKB) is a very important health indicator, especially in neonatal and perinatal care. West Java Province consistently ranks third nationally in terms of infant mortality in 2020 and 2021. This study analyzes the factors influencing infant mortality in West Java in 2022 using secondary data from the 2022 West Java Provincial Health Profile. The response variable is the number of infant deaths, while the predictor variables include the percentage of K-4 coverage (X1), high-risk pregnancy (X2), family with PHBS (X3), exclusive breastfeeding (X4), and complete immunization coverage (X5). Given the count-based nature of the data, Poisson regression was used, which assumes equidispersion where the variance is equal to the mean. However, the analysis found overdispersion, where the variance significantly exceeds the mean, making Poisson regression unsuitable. To address this, Generalized Poisson Regression (GPR) was applied, as GPR introduces a dispersion parameter that accounts for overdispersion, thus better fitting the data. The initial Poisson regression results showed that X1, X2, X4, and X5 significantly influenced infant mortality, while the GPR model showed that only X2 and X3 were significant factors, with a dispersion parameter of -3.116. The GPR model shows that every additional one high-risk pregnancy increases the infant mortality rate by 1.00006, while an increase of one unit of clean and healthy living practices reduces the mortality rate by 2.66%. Model evaluation using AIC, BIC, and RMSE confirmed that the GPR model better described the relationship between predictor variables and infant mortality rates compared to Poisson regression. These findings emphasize the need to use GPR to model cases with overdispersion in count data, so as to provide more reliable information for policy and intervention strategies.

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  • Universitas Airlangga
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Nur ChamidahUniversitas Airlangga8
Dita AmeliaUniversitas Airlangga4
Galuh Cahya NugrahaUniversitas Airlangga3
Alfi Nur NitasariUniversitas Airlangga3
Rien RecyliaUniversitas Airlangga2
Najwa Khoir AldawiyahUniversitas Airlangga2
Muhammad Fariz Fadillah MardiantoUniversitas Airlangga2
Hanny ValidaUniversitas Airlangga2
Muhammad Hafidzuddin NaharUniversitas Airlangga2
Regina FortunataUniversitas Airlangga2
Sabrina Salsa OktaviaUniversitas Airlangga2
Nabila NurdinUniversitas Airlangga2
Bryan Given Christiano GinzelUniversitas Airlangga2
Jovansha AriyawanUniversitas Airlangga2
Mohammad Noufal UbadahUniversitas Airlangga2
Gabriella Agnes BudijonoUniversitas Airlangga2
Azizah Atsariyyah ZhafiraUniversitas Airlangga2
Belindha Ayu ArdhaniUniversitas Airlangga1
Atikah FaizaUniversitas Airlangga1
Zidni Ilmatun NurrohmahUniversitas Airlangga1
Laili PuspasariUniversitas Airlangga1
Bagas Shata PratamaUniversitas Airlangga1
Alda Fuadiyah SuryonoUniversitas Airlangga1
Ardi KurniawanUniversitas Airlangga1
Mochammad Bayu FirmansyahUniversitas Airlangga1
Marisa RifadaUniversitas Airlangga1
Karina Rubita MakhbubahUniversitas Airlangga1
Devira Thania RamadhantyUniversitas Airlangga1
Any Tsalasatul FitriyahUniversitas Airlangga1
Sediono SedionoUniversitas Airlangga1
Maria Setya DewantiUniversitas Airlangga1
Aurelia Islami AzisUniversitas Airlangga1
Elly PusporaniUniversitas Airlangga1

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Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en-US">COMPARISON OF POISSON REGRESSION AND GENERALIZED POISSON REGRESSION IN MODELING THE NUMBER OF INFANT MORTALITY IN WEST JAVA 2022</dc:title>
	<dc:creator>Saifudin, Toha</dc:creator>
	<dc:creator>Salsabila, Fatiha Nadia</dc:creator>
	<dc:creator>Fitriani, Mubadi'ul</dc:creator>
	<dc:creator>Kholidiyah, Azizatul</dc:creator>
	<dc:creator>Auliyah, Nina</dc:creator>
	<dc:creator>Ariani, Fildzah Tri Januar</dc:creator>
	<dc:creator>Suliyanto, Suliyanto</dc:creator>
	<dc:description xml:lang="en-US">In line with the Sustainable Development Goals (SDGs), the Infant Mortality Rate (AKB) is a very important health indicator, especially in neonatal and perinatal care. West Java Province consistently ranks third nationally in terms of infant mortality in 2020 and 2021. This study analyzes the factors influencing infant mortality in West Java in 2022 using secondary data from the 2022 West Java Provincial Health Profile. The response variable is the number of infant deaths, while the predictor variables include the percentage of K-4 coverage (X1), high-risk pregnancy (X2), family with PHBS (X3), exclusive breastfeeding (X4), and complete immunization coverage (X5). Given the count-based nature of the data, Poisson regression was used, which assumes equidispersion where the variance is equal to the mean. However, the analysis found overdispersion, where the variance significantly exceeds the mean, making Poisson regression unsuitable. To address this, Generalized Poisson Regression (GPR) was applied, as GPR introduces a dispersion parameter that accounts for overdispersion, thus better fitting the data. The initial Poisson regression results showed that X1, X2, X4, and X5 significantly influenced infant mortality, while the GPR model showed that only X2 and X3 were significant factors, with a dispersion parameter of -3.116. The GPR model shows that every additional one high-risk pregnancy increases the infant mortality rate by 1.00006, while an increase of one unit of clean and healthy living practices reduces the mortality rate by 2.66%. Model evaluation using AIC, BIC, and RMSE confirmed that the GPR model better described the relationship between predictor variables and infant mortality rates compared to Poisson regression. These findings emphasize the need to use GPR to model cases with overdispersion in count data, so as to provide more reliable information for policy and intervention strategies.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2025-01-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12102</dc:identifier>
	<dc:identifier>10.30598/barekengvol19iss1pp35-50</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application; 35-50</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol19iss1year2025</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12102/9515</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Toha Saifudin, Fatiha Nadia Salsabila, Mubadi'ul Fitriani, Azizatul Kholidiyah, Nina Auliyah, Fildzah Tri Januar Ariani, Suliyanto Suliyanto</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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